Liesenfeld, Oliver

Abstract Sepsis is a multi-system physiological syndrome defined as a dysregulated host response to infection. Despite recent advances clinicians continue to depend on clinically or physiologically defined syndromes as poor classifiers and therefore fail to accurately predict outcome or responses to treatment. Recent studies suggest that advances in screening, diagnosis, prognosis, and treatment of sepsis can be used to tailor immunomodulatory therapies. In the present narrative review, we critically analyze important academic literature published between January 2025 and June 2026 with a focus on epidemiology, screening, diagnosis, prognosis and personalized management. We believe that sepsis care will soon be defined by the convergence of rapid diagnostics, multi-omics profiling, and artificial intelligence, enabling a shift from generalized treatment approaches toward truly personalized, dynamic patient management. While the scientific and technological foundation for this transformation is emerging rapidly, clinical validation using innovative (adaptive) clinical trial design is required. Successful implementation within routine care will require multidisciplinary collaboration, robust infrastructure, clinician and patient engagement, and healthcare systems capable of integrating innovation into practice. The coming years may see a paradigm shift in sepsis management comparable to advances observed in personalized management of malignancies.

Abstract Many patients in the emergency department present with signs and symptoms that arouse concern for sepsis; however, other explanations are also possible. There are currently no rapid tests used in clinical practice that reliably distinguish the presence of a bacterial or viral infection vs. a non-infectious etiology and can predict a patient’s likelihood to decompensate. The diagnostic and prognostic uncertainty in “gray zone” patients complicates the decision to begin therapy as clinicians need to balance the risk of withholding therapy vs. the risk of the therapy itself (e.g., overtreatment with antibiotics and hospitalization, which is costly, potentially harmful, and contributes to antibiotic resistance). The TriVerity™ Test uses isothermal amplification and machine-learning algorithms to quantify and interpret mRNA expression levels to determine both likelihood of bacterial infection, viral infection, or no infection, and whether the patient will likely require one or more critical interventions within 7 days. The three scores each fall into one of five interpretation bands ranging from Very high to Very low. Testing takes approximately 30 minutes using the proprietary Myrna™ Instrument with an operator hands-on-time of under one minute. We enrolled 1,222 patients from 22 emergency departments (ED) to validate the performance of the TriVerity Test. Patients were treated as per local standard of care and were followed for 28 days. Bacterial and viral TriVerity results were validated against clinically adjudicated infection status; the illness severity TriVerity result was validated against the need for at least one critical interventions within 7 days. The bacterial TriVerity result had high AUROC for the diagnosis of bacterial infection (0.83; 80% CI 0.81–0.85) and divided bacterial infection likelihood scores into five interpretation bands with increasing likelihood ratios of infection ranging from Very low (LR- 0.08, 80% CI 0.06–0.11) to Very high (LR + 8.04, 80% CI 5.72–11.78). The AUROC for the bacterial TriVerity result was significantly higher compared to AUROCs for C-reactive protein, procalcitonin or white blood cell count. Similarly, the viral TriVerity score showed high AUROC for the diagnosis of viral infection (0.91; 80% CI 0.90–0.93) and likelihood ratios from Very low (LR- 0.09, 80% CI 0.05–0.14) to Very high (LR + 40.93; 80% CI 29.11–79.23). The TriVerity Illness Severity score showed a high AUROC for the prediction of illness severity (0.77; 80% CI 0.77–0.81) with scores divided into five interpretation bands with increasing likelihood ratios ranging from Very low (LR- 0.22; 80% CI 0.14–0.33) to Very high (LR + 11.33; 80% CI 7.31–17.00). TriVerity illness severity results allowed marked re-classification of the risk for “ICU-level care” as compared to clinical assessment (qSOFA scores) alone. In conclusion, TriVerity provides rapid, highly accurate and actionable results for the diagnosis and prognosis of patients with suspected acute infection and/or sepsis, supporting a major unmet medical need. TriVerity may improve personalized management of patients with suspected acute infections and suspected sepsis for improved overall healthcare outcomes.